Instructions to use vidfom/Wan-T2V_14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/Wan-T2V_14B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vidfom/Wan-T2V_14B", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py from vidfom/Wan-T2V_14B: direct link, hf CLI and curl.
- Browser
- Download file 322 Bytes
-
https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py
- Command line
-
hf download hf://vidfom/Wan-T2V_14B/DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py
-
curl -L -o DiffSynth_Studio.py https://huggingface.co/vidfom/Wan-T2V_14B/resolve/main/DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py
322 Bytes
| # Set web page format | |
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| # Disable virtual VRAM on windows system | |
| import torch | |
| torch.cuda.set_per_process_memory_fraction(0.999, 0) | |
| st.markdown(""" | |
| # DiffSynth Studio | |
| [Source Code](https://github.com/Artiprocher/DiffSynth-Studio) | |
| Welcome to DiffSynth Studio. | |
| """) | |